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Forecasting Alzheimer’s Disease Using Combination Model Based on Machine Learning

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Document pages: 15 pages

Abstract: As the acceleration of aged population tendency, building models to forecastAlzheimer’s Disease (AD) is essential. In this article, we surveyed 1157 interviewees.By analyzing the results using three machine learning methods—BPneural network, SVM and random forest, we can derive the accuracy of themin forecasting AD, so that we can compare the methods in solving AD prediction.Among them, random forest is the most accurate method. Moreover, tocombine the advantages of the methods, we build a new combination forecastingmodel based on the three machine learning models, which is provedmore accurate than the models singly. At last, we give the conclusion of theconnection between life style and AD, and provide several suggestions for elderlypeople to help them prevent AD.

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